One Label Doesn’t Fit All: Self-Labeling Practices Within the Chinese Immigrant Community in Canada
Bibliographic record
Abstract
Ethnic minority populations, such as the Chinese and other racial minority communities, have traditionally been the targets of physical and social harassment. The onset of the Coronavirus (COVID-19) pandemic has reignited the racism, violence, and xenophobia faced by individuals of Chinese descent across North America. As a result, there is now a spotlight on the Chinese immigrant experience and the capacity for these individuals to authentically communicate and present their identities, specifically through the use of self-labels. In a mixed-methods investigation, we assessed the preferred self-labels among a sample of the Chinese population in Canada and sought to uncover the meanings imbued in the labels they use to describe themselves across different contexts. In addition, the relationships between label preferences and measures of ethnic identity and language were examined. Although bicultural labels (e.g., Chinese Canadian, Canadian Chinese, Hong Kong Canadian, etc.) were the most preferred, there was a variety of labels used, suggesting a more complex meaning in the choice of self-labels. Implications for identity and self-categorization are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".